outliers_mcd {Routliers} | R Documentation |
Detecting multivariate outliers using the Minimum Covariance Determinant approach
outliers_mcd(x, h, alpha, na.rm)
x |
matrix of bivariate values from which we want to compute outliers |
h |
proportion of dataset to use in order to compute sample means and covariances |
alpha |
nominal type I error probability (by default .01) |
na.rm |
set whether Missing Values should be excluded (na.rm = TRUE) or not (na.rm = FALSE) - defaults to TRUE |
Returns Call, Max distance, number of outliers
#### Run outliers_mcd # The default is to use 75% of the datasets in order to compute sample means and covariances # This proportion equals 1-breakdown points (i.e. h = .75 <--> breakdown points = .25) # This breakdown points is encouraged by Leys et al. (2018) data(Attacks) SOC <- rowMeans(Attacks[,c("soc1r","soc2r","soc3r","soc4","soc5","soc6","soc7r", "soc8","soc9","soc10r","soc11","soc12","soc13")]) HSC <- rowMeans(Attacks[,22:46]) res <- outliers_mcd(x = cbind(SOC,HSC), h = .75) res # Moreover, a list of elements can be extracted from the function, # such as the position of outliers in the dataset # and the coordinates of outliers res$outliers_pos res$outliers_val